In this paper, we study independent learners in partially observed \(N\) -player mean-field games. This finite agent model is a slight variation of the model presented in [1]}, and is closely related to the standard mean-field game model, where the set of agents is taken to be infinite. We focus on a decentralized learning environment in which players do not observe the actions of other agents and may have a limited view of the overall state of the system. Furthermore, we assume that players view only the stream of data encountered during gameplay: players do not have access to a simulator for sampling feedback data in arbitrary order, nor do they have access to any other data sets that can be used for training. In this context, we are interested in developing decentralized algorithms for MFGs that have desirable convergence properties.
